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You searched for subject:(Tile Low Rank Approximations). Showing records 1 – 30 of 16055 total matches.

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King Abdullah University of Science and Technology

1. Alharthi, Noha. Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2019, King Abdullah University of Science and Technology

 Acoustic and electromagnetic scattering from arbitrarily shaped structures can be numerically characterized by solving various surface integral equations (SIEs). One of the most effective techniques… (more)

Subjects/Keywords: Boundary Integral Equation; Acoustic Scattering; LU-Based Solver; Fast Solvers; Fast Multipole Solvers; Tile Low-Rank Approximations

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alharthi, N. (2019). Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/660105

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alharthi, Noha. “Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems.” 2019. Thesis, King Abdullah University of Science and Technology. Accessed April 18, 2021. http://hdl.handle.net/10754/660105.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alharthi, Noha. “Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems.” 2019. Web. 18 Apr 2021.

Vancouver:

Alharthi N. Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2019. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10754/660105.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alharthi N. Fast High-order Integral Equation Solvers for Acoustic and Electromagnetic Scattering Problems. [Thesis]. King Abdullah University of Science and Technology; 2019. Available from: http://hdl.handle.net/10754/660105

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Colorado

2. Fairbanks, Hillary Ruth. Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems.

Degree: PhD, 2018, University of Colorado

  Characterizing and incorporating uncertainties when simulating physical phenomena is essential for improving model-based predictions. These uncertainties may stem from a lack of knowledge regarding… (more)

Subjects/Keywords: bi-fidelity approximations; low-rank approximations; multi-fidelity approximations; parametric model reduction; uncertainty quantification; Applied Mathematics; Models and Methods

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APA (6th Edition):

Fairbanks, H. R. (2018). Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/114

Chicago Manual of Style (16th Edition):

Fairbanks, Hillary Ruth. “Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems.” 2018. Doctoral Dissertation, University of Colorado. Accessed April 18, 2021. https://scholar.colorado.edu/appm_gradetds/114.

MLA Handbook (7th Edition):

Fairbanks, Hillary Ruth. “Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems.” 2018. Web. 18 Apr 2021.

Vancouver:

Fairbanks HR. Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems. [Internet] [Doctoral dissertation]. University of Colorado; 2018. [cited 2021 Apr 18]. Available from: https://scholar.colorado.edu/appm_gradetds/114.

Council of Science Editors:

Fairbanks HR. Low-Rank, Multi-Fidelity Methods for Uncertainty Quantification of High-Dimensional Systems. [Doctoral Dissertation]. University of Colorado; 2018. Available from: https://scholar.colorado.edu/appm_gradetds/114


Universidade Nova

3. Rodrigues, Paulo Jorge Canas. New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions.

Degree: 2012, Universidade Nova

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco, especialidade em Estatística

Genotype-by-environment interaction (GEI) is frequent in multi-environment trials, and… (more)

Subjects/Keywords: Genotype-by-environment interaction; QTL-by-environment interaction; AMMI models; Low-rank approximations; Weighted low-rank approximations; Eco-physiological crop growth models

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APA (6th Edition):

Rodrigues, P. J. C. (2012). New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rodrigues, Paulo Jorge Canas. “New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions.” 2012. Thesis, Universidade Nova. Accessed April 18, 2021. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rodrigues, Paulo Jorge Canas. “New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions.” 2012. Web. 18 Apr 2021.

Vancouver:

Rodrigues PJC. New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions. [Internet] [Thesis]. Universidade Nova; 2012. [cited 2021 Apr 18]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rodrigues PJC. New strategies to detect and understand genotype-by-environment interactions and QTL-by-environment interactions. [Thesis]. Universidade Nova; 2012. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/7848

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


King Abdullah University of Science and Technology

4. Charara, Ali. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2018, King Abdullah University of Science and Technology

 Covariance matrices are ubiquitous in computational sciences, typically describing the correlation of elements of large multivariate spatial data sets. For example, covari- ance matrices are… (more)

Subjects/Keywords: data sparse; Hierarchical; covariance matrix; GPU; tile low-rank; Dense Linear Algebra

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APA (6th Edition):

Charara, A. (2018). Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/627948

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Charara, Ali. “Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems.” 2018. Thesis, King Abdullah University of Science and Technology. Accessed April 18, 2021. http://hdl.handle.net/10754/627948.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Charara, Ali. “Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems.” 2018. Web. 18 Apr 2021.

Vancouver:

Charara A. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/10754/627948.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Charara A. Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems. [Thesis]. King Abdullah University of Science and Technology; 2018. Available from: http://hdl.handle.net/10754/627948

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

5. Ayala Obregón, Alan. Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace.

Degree: Docteur es, Mathématiques appliquées, 2018, Sorbonne université

L'objectif de cette thèse est de fournir des techniques de réduction de complexité pour la solution des équations intégrales de frontière (BIE). En particulier, nous… (more)

Subjects/Keywords: Formulation multi-trace; Équation de Maxwell; Rang faible; Sous-espaces affines; Algorithme de communication optimale; Approximation CUR; Multi-trace formulation; Maxwell equations; Low-rank approximations

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APA (6th Edition):

Ayala Obregón, A. (2018). Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace. (Doctoral Dissertation). Sorbonne université. Retrieved from http://www.theses.fr/2018SORUS581

Chicago Manual of Style (16th Edition):

Ayala Obregón, Alan. “Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace.” 2018. Doctoral Dissertation, Sorbonne université. Accessed April 18, 2021. http://www.theses.fr/2018SORUS581.

MLA Handbook (7th Edition):

Ayala Obregón, Alan. “Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace.” 2018. Web. 18 Apr 2021.

Vancouver:

Ayala Obregón A. Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace. [Internet] [Doctoral dissertation]. Sorbonne université; 2018. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2018SORUS581.

Council of Science Editors:

Ayala Obregón A. Complexity reduction methods applied to the rapid solution to multi-trace boundary integral formulations : Méthodes de réduction de complexité appliquées à la résolution rapide des formulations intégrales de bord de type multi-trace. [Doctoral Dissertation]. Sorbonne université; 2018. Available from: http://www.theses.fr/2018SORUS581


INP Toulouse

6. Weisbecker, Clément. Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs.

Degree: Docteur es, Sûreté du logiciel et calcul haute performance, 2013, INP Toulouse

 Nous considérons la résolution de très grands systèmes linéaires creux à l'aide d'une méthode de factorisation directe appelée méthode multifrontale. Bien que numériquement robustes et… (more)

Subjects/Keywords: Matrices creuses; Systèmes linéaires creux; Méthodes directes; Méthode multifrontale; Approximations rang-faible; Equations aux dérivées partielles elliptiques; Sparse matrices; Direct methods for linear systems; Multifrontal method; Low-rank approximations; High-performance computing; Parallel computing

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APA (6th Edition):

Weisbecker, C. (2013). Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2013INPT0134

Chicago Manual of Style (16th Edition):

Weisbecker, Clément. “Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs.” 2013. Doctoral Dissertation, INP Toulouse. Accessed April 18, 2021. http://www.theses.fr/2013INPT0134.

MLA Handbook (7th Edition):

Weisbecker, Clément. “Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs.” 2013. Web. 18 Apr 2021.

Vancouver:

Weisbecker C. Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs. [Internet] [Doctoral dissertation]. INP Toulouse; 2013. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2013INPT0134.

Council of Science Editors:

Weisbecker C. Improving multifrontal solvers by means of algebraic Block Low-Rank representations : Amélioration des solveurs multifrontaux à l’aide de representations algébriques rang-faible par blocs. [Doctoral Dissertation]. INP Toulouse; 2013. Available from: http://www.theses.fr/2013INPT0134

7. Mary, Théo. Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability.

Degree: Docteur es, Mathématiques, 2017, Université Toulouse III – Paul Sabatier

Nous nous intéressons à l'utilisation d'approximations de rang faible pour réduire le coût des solveurs creux directs multifrontaux. Parmi les différents formats matriciels qui ont… (more)

Subjects/Keywords: Matrices creuses; Systèmes linéaires creux; Méthodes directes; Méthode multifrontale; Approximations de rang-faible; Equations aux dérivées partielles elliptiques; Calcul haute performance; Calcul parallèle; Sparse matrices; Direct methods for linear systems; Multifrontal method; Low-rank approximations; High-performance computing; Parallel computing; Partial differential equations

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APA (6th Edition):

Mary, T. (2017). Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability. (Doctoral Dissertation). Université Toulouse III – Paul Sabatier. Retrieved from http://www.theses.fr/2017TOU30305

Chicago Manual of Style (16th Edition):

Mary, Théo. “Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability.” 2017. Doctoral Dissertation, Université Toulouse III – Paul Sabatier. Accessed April 18, 2021. http://www.theses.fr/2017TOU30305.

MLA Handbook (7th Edition):

Mary, Théo. “Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability.” 2017. Web. 18 Apr 2021.

Vancouver:

Mary T. Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability. [Internet] [Doctoral dissertation]. Université Toulouse III – Paul Sabatier; 2017. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2017TOU30305.

Council of Science Editors:

Mary T. Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme : Block low-rank multifrontal solvers : complexity, performance, and scalability. [Doctoral Dissertation]. Université Toulouse III – Paul Sabatier; 2017. Available from: http://www.theses.fr/2017TOU30305


Georgia Tech

8. Hayes, Charles Ethan. Low-rank model exploitation of electromagnetic induction sensors.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

 The objective of this research is to improve the signal processing of electromagnetic induction (EMI) sensors for detection, localization, characterization, and classification of targets buried… (more)

Subjects/Keywords: EMI; Electromagnetic induction; Low rank

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APA (6th Edition):

Hayes, C. E. (2020). Low-rank model exploitation of electromagnetic induction sensors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63587

Chicago Manual of Style (16th Edition):

Hayes, Charles Ethan. “Low-rank model exploitation of electromagnetic induction sensors.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/63587.

MLA Handbook (7th Edition):

Hayes, Charles Ethan. “Low-rank model exploitation of electromagnetic induction sensors.” 2020. Web. 18 Apr 2021.

Vancouver:

Hayes CE. Low-rank model exploitation of electromagnetic induction sensors. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/63587.

Council of Science Editors:

Hayes CE. Low-rank model exploitation of electromagnetic induction sensors. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63587


Georgia Tech

9. Xing, Xin. The proxy point method for rank-structured matrices.

Degree: PhD, Mathematics, 2019, Georgia Tech

Rank-structured matrix representations, e.g., \mathcal{H}2 and HSS, are commonly used to reduce computation and storage cost for dense matrices defined by interactions between many bodies.… (more)

Subjects/Keywords: Rank-structured matrices; Low-rank approximation; Kernel matrices; Numerical linear algebra

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APA (6th Edition):

Xing, X. (2019). The proxy point method for rank-structured matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62327

Chicago Manual of Style (16th Edition):

Xing, Xin. “The proxy point method for rank-structured matrices.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/62327.

MLA Handbook (7th Edition):

Xing, Xin. “The proxy point method for rank-structured matrices.” 2019. Web. 18 Apr 2021.

Vancouver:

Xing X. The proxy point method for rank-structured matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/62327.

Council of Science Editors:

Xing X. The proxy point method for rank-structured matrices. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62327

10. Löffler, Matthias. Statistical inference in high-dimensional matrix models.

Degree: PhD, 2020, University of Cambridge

 Matrix models are ubiquitous in modern statistics. For instance, they are used in finance to assess interdependence of assets, in genomics to impute missing data… (more)

Subjects/Keywords: High-dimensional Statistics; Low-rank inference; PCA

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APA (6th Edition):

Löffler, M. (2020). Statistical inference in high-dimensional matrix models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/298064

Chicago Manual of Style (16th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 18, 2021. https://www.repository.cam.ac.uk/handle/1810/298064.

MLA Handbook (7th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Web. 18 Apr 2021.

Vancouver:

Löffler M. Statistical inference in high-dimensional matrix models. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/298064.

Council of Science Editors:

Löffler M. Statistical inference in high-dimensional matrix models. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/298064


University of Illinois – Urbana-Champaign

11. Gui, Huan. Low-rank estimation and embedding learning: theory and applications.

Degree: PhD, Computer Science, 2017, University of Illinois – Urbana-Champaign

 In many real-world applications of data mining, datasets can be represented using matrices, where rows of the matrix correspond to objects (or data instances) and… (more)

Subjects/Keywords: Low-rank model; Embedding learning; Noncovex

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APA (6th Edition):

Gui, H. (2017). Low-rank estimation and embedding learning: theory and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98280

Chicago Manual of Style (16th Edition):

Gui, Huan. “Low-rank estimation and embedding learning: theory and applications.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/98280.

MLA Handbook (7th Edition):

Gui, Huan. “Low-rank estimation and embedding learning: theory and applications.” 2017. Web. 18 Apr 2021.

Vancouver:

Gui H. Low-rank estimation and embedding learning: theory and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/98280.

Council of Science Editors:

Gui H. Low-rank estimation and embedding learning: theory and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98280


University of Cambridge

12. Löffler, Matthias. Statistical inference in high-dimensional matrix models.

Degree: PhD, 2020, University of Cambridge

 Matrix models are ubiquitous in modern statistics. For instance, they are used in finance to assess interdependence of assets, in genomics to impute missing data… (more)

Subjects/Keywords: High-dimensional Statistics; Low-rank inference; PCA

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APA (6th Edition):

Löffler, M. (2020). Statistical inference in high-dimensional matrix models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044

Chicago Manual of Style (16th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 18, 2021. https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044.

MLA Handbook (7th Edition):

Löffler, Matthias. “Statistical inference in high-dimensional matrix models.” 2020. Web. 18 Apr 2021.

Vancouver:

Löffler M. Statistical inference in high-dimensional matrix models. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 18]. Available from: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044.

Council of Science Editors:

Löffler M. Statistical inference in high-dimensional matrix models. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.45122 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793044


University of Iowa

13. Bhattacharya, Ipshita. Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Iowa

  Non-invasively reosolving spatial distribution of tissue metabolites serves as a diagnostic tool to in-vivo metabolism thus making magnetic resonance spectroscopic imaging (MRSI) a very… (more)

Subjects/Keywords: Low-rank based algorithms; Magnetic Resonance Imaging; Optimization Algorithm; Spectroscopy; Structured low-rank; Electrical and Computer Engineering

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APA (6th Edition):

Bhattacharya, I. (2017). Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/6058

Chicago Manual of Style (16th Edition):

Bhattacharya, Ipshita. “Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm.” 2017. Doctoral Dissertation, University of Iowa. Accessed April 18, 2021. https://ir.uiowa.edu/etd/6058.

MLA Handbook (7th Edition):

Bhattacharya, Ipshita. “Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm.” 2017. Web. 18 Apr 2021.

Vancouver:

Bhattacharya I. Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm. [Internet] [Doctoral dissertation]. University of Iowa; 2017. [cited 2021 Apr 18]. Available from: https://ir.uiowa.edu/etd/6058.

Council of Science Editors:

Bhattacharya I. Pushing the limits of spectroscopic imaging using novel low-rank based reconstruction algorithm. [Doctoral Dissertation]. University of Iowa; 2017. Available from: https://ir.uiowa.edu/etd/6058


Rochester Institute of Technology

14. Song, Ge. Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points.

Degree: MS, 2019, Rochester Institute of Technology

  The human brain is hard to study and analysis, not because of the complexity of the brain structure, such as neurons and neurons connections,… (more)

Subjects/Keywords: Brain regions; Brain scans; fMRI; Low-rank multivariate general linear model

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APA (6th Edition):

Song, G. (2019). Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10310

Chicago Manual of Style (16th Edition):

Song, Ge. “Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed April 18, 2021. https://scholarworks.rit.edu/theses/10310.

MLA Handbook (7th Edition):

Song, Ge. “Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points.” 2019. Web. 18 Apr 2021.

Vancouver:

Song G. Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Apr 18]. Available from: https://scholarworks.rit.edu/theses/10310.

Council of Science Editors:

Song G. Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10310


Temple University

15. Shank, Stephen David. Low-rank solution methods for large-scale linear matrix equations.

Degree: PhD, 2014, Temple University

Mathematics

We consider low-rank solution methods for certain classes of large-scale linear matrix equations. Our aim is to adapt existing low-rank solution methods based on… (more)

Subjects/Keywords: Applied mathematics;

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shank, S. D. (2014). Low-rank solution methods for large-scale linear matrix equations. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,273331

Chicago Manual of Style (16th Edition):

Shank, Stephen David. “Low-rank solution methods for large-scale linear matrix equations.” 2014. Doctoral Dissertation, Temple University. Accessed April 18, 2021. http://digital.library.temple.edu/u?/p245801coll10,273331.

MLA Handbook (7th Edition):

Shank, Stephen David. “Low-rank solution methods for large-scale linear matrix equations.” 2014. Web. 18 Apr 2021.

Vancouver:

Shank SD. Low-rank solution methods for large-scale linear matrix equations. [Internet] [Doctoral dissertation]. Temple University; 2014. [cited 2021 Apr 18]. Available from: http://digital.library.temple.edu/u?/p245801coll10,273331.

Council of Science Editors:

Shank SD. Low-rank solution methods for large-scale linear matrix equations. [Doctoral Dissertation]. Temple University; 2014. Available from: http://digital.library.temple.edu/u?/p245801coll10,273331


University of Alberta

16. Li,Qiang. Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries.

Degree: MS, Department of Chemical and Materials Engineering, 2014, University of Alberta

 The objective of this research is to understand the effect of hydrothermal dewatering (HTD) on surface properties, stability and rheological behavior of lignite water slurry… (more)

Subjects/Keywords: Lignite; coal water slurry; Rheology; Low rank coal; Hydrothermal treatment

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APA (6th Edition):

Li,Qiang. (2014). Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/xs55mc577

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Li,Qiang. “Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries.” 2014. Masters Thesis, University of Alberta. Accessed April 18, 2021. https://era.library.ualberta.ca/files/xs55mc577.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Li,Qiang. “Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries.” 2014. Web. 18 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Li,Qiang. Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries. [Internet] [Masters thesis]. University of Alberta; 2014. [cited 2021 Apr 18]. Available from: https://era.library.ualberta.ca/files/xs55mc577.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Li,Qiang. Hydrothermal Treatment of Low Rank Coal for Making High Solid Loading and Stable Coal Water Slurries. [Masters Thesis]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/xs55mc577

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


Georgia Tech

17. Yang, Mengmeng. Seismic imaging with extended image volumes and source estimation.

Degree: PhD, Earth and Atmospheric Sciences, 2020, Georgia Tech

 Seismic imaging is an important tool for the exploration and production of oil & gas, carbon sequestration, and the mitigation of geohazards. Through the process… (more)

Subjects/Keywords: Extended image volumes; Low rank; Sparsity; Source estimation; Multiples

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APA (6th Edition):

Yang, M. (2020). Seismic imaging with extended image volumes and source estimation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62832

Chicago Manual of Style (16th Edition):

Yang, Mengmeng. “Seismic imaging with extended image volumes and source estimation.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/62832.

MLA Handbook (7th Edition):

Yang, Mengmeng. “Seismic imaging with extended image volumes and source estimation.” 2020. Web. 18 Apr 2021.

Vancouver:

Yang M. Seismic imaging with extended image volumes and source estimation. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/62832.

Council of Science Editors:

Yang M. Seismic imaging with extended image volumes and source estimation. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62832


University of California – Berkeley

18. Ong, Frank. Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging.

Degree: Electrical Engineering & Computer Sciences, 2018, University of California – Berkeley

 Magnetic Resonance Imaging (MRI) is an amazing imaging modality in many aspects. It offers one of the best imaging contrast for visualizing soft issues. It… (more)

Subjects/Keywords: Electrical engineering; Compressed sensing; Dynamic Imaging; Low rank; MRI

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APA (6th Edition):

Ong, F. (2018). Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/27d0k54z

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ong, Frank. “Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging.” 2018. Thesis, University of California – Berkeley. Accessed April 18, 2021. http://www.escholarship.org/uc/item/27d0k54z.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ong, Frank. “Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging.” 2018. Web. 18 Apr 2021.

Vancouver:

Ong F. Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging. [Internet] [Thesis]. University of California – Berkeley; 2018. [cited 2021 Apr 18]. Available from: http://www.escholarship.org/uc/item/27d0k54z.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ong F. Low Dimensional Methods for High Dimensional Magnetic Resonance Imaging. [Thesis]. University of California – Berkeley; 2018. Available from: http://www.escholarship.org/uc/item/27d0k54z

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

19. Torchio, Riccardo. Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems.

Degree: Docteur es, Génie électrique, 2019, Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie)

L'objectif principal de cette thèse est d'étendre et d'améliorer la précision de la méthode des circuits équivalents à éléments partiels non structurés (Unstructured PEEC). L'intérêt… (more)

Subjects/Keywords: Formulations; Intégrale; Électromagnétiques; Stochastique; Integral; Electromagnetic; Formulation; Peec; Low-Rank; 620

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APA (6th Edition):

Torchio, R. (2019). Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems. (Doctoral Dissertation). Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie). Retrieved from http://www.theses.fr/2019GREAT066

Chicago Manual of Style (16th Edition):

Torchio, Riccardo. “Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems.” 2019. Doctoral Dissertation, Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie). Accessed April 18, 2021. http://www.theses.fr/2019GREAT066.

MLA Handbook (7th Edition):

Torchio, Riccardo. “Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems.” 2019. Web. 18 Apr 2021.

Vancouver:

Torchio R. Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie); 2019. [cited 2021 Apr 18]. Available from: http://www.theses.fr/2019GREAT066.

Council of Science Editors:

Torchio R. Une extension de la méthode PEEC non structurée aux problèmes magnétiques, temporels et stochastiques : Extending the Unstructured PEEC Method to Magnetic, Transient, and Stochastic Electromagnetic Problems. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); Università degli studi (Padoue, Italie); 2019. Available from: http://www.theses.fr/2019GREAT066


Colorado School of Mines

20. Yang, Dehui. Structured low-rank matrix recovery via optimization methods.

Degree: PhD, Electrical Engineering, 2018, Colorado School of Mines

 From single-molecule microscopy in biology, to collaborative filtering in recommendation systems, to quantum state tomography in physics, many scientific discoveries involve solving ill-posed inverse problems,… (more)

Subjects/Keywords: matrix completion; models; super-resolution; modal analysis; low-rank; optimization

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APA (6th Edition):

Yang, D. (2018). Structured low-rank matrix recovery via optimization methods. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172154

Chicago Manual of Style (16th Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Doctoral Dissertation, Colorado School of Mines. Accessed April 18, 2021. http://hdl.handle.net/11124/172154.

MLA Handbook (7th Edition):

Yang, Dehui. “Structured low-rank matrix recovery via optimization methods.” 2018. Web. 18 Apr 2021.

Vancouver:

Yang D. Structured low-rank matrix recovery via optimization methods. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2018. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/11124/172154.

Council of Science Editors:

Yang D. Structured low-rank matrix recovery via optimization methods. [Doctoral Dissertation]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172154


University of Minnesota

21. Jiang, Bo. Polynomial optimization: structures, algorithms, and engineering applications.

Degree: PhD, Industrial and Systems Engineering, 2013, University of Minnesota

 As a fundamental model in Operations Research, polynomial optimization has been receiving increasingly more attention in the recent years, due to its versatile modern applications… (more)

Subjects/Keywords: Approximation algorithms; Low-rank; Polynomial optimization; Tensor optimization

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APA (6th Edition):

Jiang, B. (2013). Polynomial optimization: structures, algorithms, and engineering applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/159747

Chicago Manual of Style (16th Edition):

Jiang, Bo. “Polynomial optimization: structures, algorithms, and engineering applications.” 2013. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021. http://purl.umn.edu/159747.

MLA Handbook (7th Edition):

Jiang, Bo. “Polynomial optimization: structures, algorithms, and engineering applications.” 2013. Web. 18 Apr 2021.

Vancouver:

Jiang B. Polynomial optimization: structures, algorithms, and engineering applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2021 Apr 18]. Available from: http://purl.umn.edu/159747.

Council of Science Editors:

Jiang B. Polynomial optimization: structures, algorithms, and engineering applications. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/159747


University of Minnesota

22. Guhaniyogi, Rajarshi. On Bayesian hierarchical modelling for large spatial datasets.

Degree: PhD, Biostatistics, 2012, University of Minnesota

 We propose a class of fully process-based low-rank spatially-varying cross-covariance matrices that produce non-degenerate spatial processes and that effectively capture non-stationary covariances among the multiple… (more)

Subjects/Keywords: Hierarchical Bayesiam Model; Low rank Models; Predictive processes; Spatial statistics; Biostatistics

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APA (6th Edition):

Guhaniyogi, R. (2012). On Bayesian hierarchical modelling for large spatial datasets. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/122854

Chicago Manual of Style (16th Edition):

Guhaniyogi, Rajarshi. “On Bayesian hierarchical modelling for large spatial datasets.” 2012. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021. http://purl.umn.edu/122854.

MLA Handbook (7th Edition):

Guhaniyogi, Rajarshi. “On Bayesian hierarchical modelling for large spatial datasets.” 2012. Web. 18 Apr 2021.

Vancouver:

Guhaniyogi R. On Bayesian hierarchical modelling for large spatial datasets. [Internet] [Doctoral dissertation]. University of Minnesota; 2012. [cited 2021 Apr 18]. Available from: http://purl.umn.edu/122854.

Council of Science Editors:

Guhaniyogi R. On Bayesian hierarchical modelling for large spatial datasets. [Doctoral Dissertation]. University of Minnesota; 2012. Available from: http://purl.umn.edu/122854


University of Minnesota

23. Mardani, Morteza. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.

Degree: PhD, Electrical/Computer Engineering, 2015, University of Minnesota

 We live in an era of ``data deluge," with pervasive sensors collecting massive amounts of information on every bit of our lives, churning out enormous… (more)

Subjects/Keywords: Big data; Large-scale networks; learning; Low rank; Sparsity

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APA (6th Edition):

Mardani, M. (2015). Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/174873

Chicago Manual of Style (16th Edition):

Mardani, Morteza. “Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.” 2015. Doctoral Dissertation, University of Minnesota. Accessed April 18, 2021. http://hdl.handle.net/11299/174873.

MLA Handbook (7th Edition):

Mardani, Morteza. “Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.” 2015. Web. 18 Apr 2021.

Vancouver:

Mardani M. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/11299/174873.

Council of Science Editors:

Mardani M. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/174873


Georgia Tech

24. Rangel Walteros, Pedro Andres. A non-asymptotic study of low-rank estimation of smooth kernels on graphs.

Degree: PhD, Mathematics, 2014, Georgia Tech

 This dissertation investigates the problem of estimating a kernel over a large graph based on a sample of noisy observations of linear measurements of the… (more)

Subjects/Keywords: Low-rank matrix completion; Kernels on graphs; High dimensional probability

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APA (6th Edition):

Rangel Walteros, P. A. (2014). A non-asymptotic study of low-rank estimation of smooth kernels on graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52988

Chicago Manual of Style (16th Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/52988.

MLA Handbook (7th Edition):

Rangel Walteros, Pedro Andres. “A non-asymptotic study of low-rank estimation of smooth kernels on graphs.” 2014. Web. 18 Apr 2021.

Vancouver:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/52988.

Council of Science Editors:

Rangel Walteros PA. A non-asymptotic study of low-rank estimation of smooth kernels on graphs. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52988


Georgia Tech

25. Kannan, Ramakrishnan. Scalable and distributed constrained low rank approximations.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

Low rank approximation is the problem of finding two low rank factors W and H such that the rank(WH) << rank(A) and A ≈ WH.… (more)

Subjects/Keywords: Distributed; Scalable; NMF; Communication avoiding; HPC; Low rank approximation

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APA (6th Edition):

Kannan, R. (2016). Scalable and distributed constrained low rank approximations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54962

Chicago Manual of Style (16th Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/54962.

MLA Handbook (7th Edition):

Kannan, Ramakrishnan. “Scalable and distributed constrained low rank approximations.” 2016. Web. 18 Apr 2021.

Vancouver:

Kannan R. Scalable and distributed constrained low rank approximations. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/54962.

Council of Science Editors:

Kannan R. Scalable and distributed constrained low rank approximations. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54962


Georgia Tech

26. Xia, Dong. Statistical inference for large matrices.

Degree: PhD, Mathematics, 2016, Georgia Tech

 This thesis covers two topics on matrix analysis and estimation in machine learning and statistics. The first topic is about density matrix estimation with application… (more)

Subjects/Keywords: Low rank; Matrix estimation; Singular vectors; Random perturbation

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APA (6th Edition):

Xia, D. (2016). Statistical inference for large matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55632

Chicago Manual of Style (16th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/55632.

MLA Handbook (7th Edition):

Xia, Dong. “Statistical inference for large matrices.” 2016. Web. 18 Apr 2021.

Vancouver:

Xia D. Statistical inference for large matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/55632.

Council of Science Editors:

Xia D. Statistical inference for large matrices. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55632


University of Texas – Austin

27. -0961-6947. Seismic modeling and imaging in complex media using low-rank approximation.

Degree: PhD, Geological Sciences, 2016, University of Texas – Austin

 Seismic imaging in geologically complex areas, such as sub-salt or attenuating areas, has been one of the greatest challenges in hydrocarbon exploration. Increasing the fidelity… (more)

Subjects/Keywords: Seismic modeling; Reverse-time migration; Low-rank approximation; Seismic attenuation

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APA (6th Edition):

-0961-6947. (2016). Seismic modeling and imaging in complex media using low-rank approximation. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/45954

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-0961-6947. “Seismic modeling and imaging in complex media using low-rank approximation.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed April 18, 2021. http://hdl.handle.net/2152/45954.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-0961-6947. “Seismic modeling and imaging in complex media using low-rank approximation.” 2016. Web. 18 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0961-6947. Seismic modeling and imaging in complex media using low-rank approximation. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2152/45954.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-0961-6947. Seismic modeling and imaging in complex media using low-rank approximation. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/45954

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


Purdue University

28. Hou, Yangyang. Low rank methods for optimizing clustering.

Degree: PhD, Computer Science, 2016, Purdue University

  Complex optimization models and problems in machine learning often have the majority of information in a low rank subspace. By careful exploitation of these… (more)

Subjects/Keywords: Applied sciences; Clustering; Low rank methods; Computer Sciences

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APA (6th Edition):

Hou, Y. (2016). Low rank methods for optimizing clustering. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/935

Chicago Manual of Style (16th Edition):

Hou, Yangyang. “Low rank methods for optimizing clustering.” 2016. Doctoral Dissertation, Purdue University. Accessed April 18, 2021. https://docs.lib.purdue.edu/open_access_dissertations/935.

MLA Handbook (7th Edition):

Hou, Yangyang. “Low rank methods for optimizing clustering.” 2016. Web. 18 Apr 2021.

Vancouver:

Hou Y. Low rank methods for optimizing clustering. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2021 Apr 18]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/935.

Council of Science Editors:

Hou Y. Low rank methods for optimizing clustering. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/935


Georgia Tech

29. Sharan, Shashin. LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION.

Degree: PhD, Earth and Atmospheric Sciences, 2020, Georgia Tech

 Seismic data reconstruction on a dense periodic grid from seismic data acquired on a coarse grid is a common approach followed by most of the… (more)

Subjects/Keywords: Sparsity-promoting; Low-Rank; Wavefield Reconstruction; Compressed Sensing

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APA (6th Edition):

Sharan, S. (2020). LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64162

Chicago Manual of Style (16th Edition):

Sharan, Shashin. “LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION.” 2020. Doctoral Dissertation, Georgia Tech. Accessed April 18, 2021. http://hdl.handle.net/1853/64162.

MLA Handbook (7th Edition):

Sharan, Shashin. “LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION.” 2020. Web. 18 Apr 2021.

Vancouver:

Sharan S. LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/1853/64162.

Council of Science Editors:

Sharan S. LARGE SCALE HIGH-FREQUENCY SEISMIC WAVEFIELD RECONSTRUCTION, ACQUISITION VIA RANK MINIMIZATION AND SPARSITY-PROMOTING SOURCE ESTIMATION. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64162


University of Cambridge

30. Maji, Partha. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.

Degree: PhD, 2020, University of Cambridge

 In deep learning, a convolutional neural network (ConvNet or CNN) is a powerful tool for building interesting embedded applications that use data to make predictions.… (more)

Subjects/Keywords: Neural Network; Convolutional Neural Network; Optimisation; SIMD; Low-rank; Compression; Accelerators

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APA (6th Edition):

Maji, P. (2020). Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/307488

Chicago Manual of Style (16th Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 18, 2021. https://www.repository.cam.ac.uk/handle/1810/307488.

MLA Handbook (7th Edition):

Maji, Partha. “Model-Architecture Co-design of Deep Neural Networks for Embedded Systems.” 2020. Web. 18 Apr 2021.

Vancouver:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/307488.

Council of Science Editors:

Maji P. Model-Architecture Co-design of Deep Neural Networks for Embedded Systems. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/307488

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